{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from tqdm import tqdm\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import models\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = 'cuda:0'\n",
    "BATCH_SIZE = 1\n",
    "EPOCHS = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## Define Teacher and Student Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrained_vgg = models.vgg19(pretrained=True)\n",
    "teacher = pretrained_vgg.features[:36]\n",
    "teacher = teacher.to(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg = models.vgg19(pretrained=False)\n",
    "student = vgg.features[:36]\n",
    "student = student.to(DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## Prepare Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose([transforms.Grayscale(3), # Match the mnist channel dim with vgg19 input\n",
    "                                transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.5, ), (0.5, ))\n",
    "                               ])\n",
    "\n",
    "mnist = datasets.MNIST(root='/dgx/inoue/data', \n",
    "                       train=True, \n",
    "                       download=True,\n",
    "                       transform=transform)\n",
    "\n",
    "train_loader = DataLoader(dataset=mnist,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## Train Student Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.MSELoss(reduction='mean')\n",
    "optimizer = torch.optim.Adam(student.parameters(), lr=0.0002, weight_decay=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [22:25<00:00, 134.53s/it]\n"
     ]
    }
   ],
   "source": [
    "teacher.eval()\n",
    "\n",
    "for epoch in tqdm(range(EPOCHS)):\n",
    "    for i, (img, digit) in enumerate(train_loader):\n",
    "         \n",
    "        if digit == 1:\n",
    "            img = img.to(DEVICE)\n",
    "            with torch.no_grad():\n",
    "                surrogate_label = teacher(img)\n",
    "            optimizer.zero_grad()\n",
    "            pred = student(img)\n",
    "            loss = criterion(pred, surrogate_label)\n",
    "            loss.backward()\n",
    "            optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## Measure Anomaly Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60000/60000 [05:42<00:00, 175.11it/s]\n"
     ]
    }
   ],
   "source": [
    "teacher.eval()\n",
    "student.eval()\n",
    "\n",
    "losses = []\n",
    "digits = []\n",
    "for img, digit in tqdm(train_loader):\n",
    "    with torch.no_grad():\n",
    "        img = img.to(DEVICE)\n",
    "        surrogate_label = teacher(img)\n",
    "        pred = student(img)\n",
    "        loss = criterion(pred, surrogate_label)\n",
    "        \n",
    "        losses.append(loss.item())\n",
    "        digits.append(digit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/miniconda/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:16: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x2880 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "losses = np.array(losses)\n",
    "digits = np.array(digits)\n",
    "\n",
    "plt.figure(figsize=(8, 40))\n",
    "for i in range(10):\n",
    "    plt.subplot(11, 1, i+1)\n",
    "    plt.hist(losses[digits==i], range=(0, 0.03), label=str(i), bins=100, color='k', alpha=0.4)\n",
    "    plt.legend()\n",
    "    plt.ylabel('Frequency')\n",
    "\n",
    "    \n",
    "    plt.subplot(11, 1, 11)\n",
    "    color = cm.hsv(i/10)\n",
    "    plt.hist(losses[digits==i], range=(0, 0.03), label=str(i), bins=100, color=color, alpha=0.4)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('L2 Loss')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
